Overview

Dataset statistics

Number of variables20
Number of observations176573
Missing cells176543
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 MiB
Average record size in memory160.0 B

Variable types

Numeric8
Categorical12

Warnings

batsman has a high cardinality: 514 distinct values High cardinality
bowler has a high cardinality: 404 distinct values High cardinality
non_striker has a high cardinality: 509 distinct values High cardinality
player_out has a high cardinality: 487 distinct values High cardinality
fielder_caught_out has a high cardinality: 509 distinct values High cardinality
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
extras_wides is highly correlated with total_extras_runsHigh correlation
extras_legbyes is highly correlated with total_extras_runsHigh correlation
total_extras_runs is highly correlated with extras_wides and 1 other fieldsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
total_runs is highly correlated with batsman_runsHigh correlation
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
extras_wides is highly correlated with total_extras_runsHigh correlation
extras_legbyes is highly correlated with total_extras_runsHigh correlation
total_extras_runs is highly correlated with extras_wides and 1 other fieldsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
total_runs is highly correlated with batsman_runsHigh correlation
id is highly correlated with seasonHigh correlation
season is highly correlated with idHigh correlation
extras_wides is highly correlated with total_extras_runsHigh correlation
extras_legbyes is highly correlated with total_extras_runsHigh correlation
total_extras_runs is highly correlated with extras_wides and 1 other fieldsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
total_runs is highly correlated with batsman_runsHigh correlation
total_extras_runs is highly correlated with total_runs and 3 other fieldsHigh correlation
innings is highly correlated with replacementsHigh correlation
bowled_over is highly correlated with replacementsHigh correlation
total_runs is highly correlated with total_extras_runs and 3 other fieldsHigh correlation
type_out is highly correlated with replacementsHigh correlation
extras_legbyes is highly correlated with total_extras_runsHigh correlation
season is highly correlated with replacements and 1 other fieldsHigh correlation
replacements is highly correlated with innings and 7 other fieldsHigh correlation
extras_penalty is highly correlated with total_extras_runsHigh correlation
batsman_team is highly correlated with replacementsHigh correlation
batsman_runs is highly correlated with total_runs and 1 other fieldsHigh correlation
id is highly correlated with season and 1 other fieldsHigh correlation
extras_wides is highly correlated with total_extras_runs and 1 other fieldsHigh correlation
extras_noballs is highly correlated with replacementsHigh correlation
replacements is highly correlated with extras_noballs and 5 other fieldsHigh correlation
extras_byes is highly correlated with replacementsHigh correlation
extras_penalty is highly correlated with replacementsHigh correlation
batsman_team is highly correlated with replacementsHigh correlation
innings is highly correlated with replacementsHigh correlation
type_out is highly correlated with replacementsHigh correlation
replacements has 176543 (> 99.9%) missing values Missing
replacements is uniformly distributed Uniform
extras_wides has 171230 (97.0%) zeros Zeros
extras_legbyes has 173664 (98.4%) zeros Zeros
total_extras_runs has 167142 (94.7%) zeros Zeros
batsman_runs has 71130 (40.3%) zeros Zeros
total_runs has 62100 (35.2%) zeros Zeros

Reproduction

Analysis started2021-09-17 17:19:35.749448
Analysis finished2021-09-17 17:20:46.657144
Duration1 minute and 10.91 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct746
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean713160.0962
Minimum335982
Maximum1178425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:20:46.954349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336019
Q1501208
median598047
Q3980985
95-th percentile1175368
Maximum1178425
Range842443
Interquartile range (IQR)479777

Descriptive statistics

Standard deviation284366.5362
Coefficient of variation (CV)0.3987415135
Kurtosis-1.33600124
Mean713160.0962
Median Absolute Deviation (MAD)205835
Skewness0.3585860896
Sum1.259248177 × 1011
Variance8.086432689 × 1010
MonotonicityNot monotonic
2021-09-17T12:20:47.418113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
829737262
 
0.1%
829811259
 
0.1%
1178423257
 
0.1%
419142257
 
0.1%
734047257
 
0.1%
501221257
 
0.1%
548367256
 
0.1%
829805256
 
0.1%
392190256
 
0.1%
829777255
 
0.1%
Other values (736)174001
98.5%
ValueCountFrequency (%)
335982225
0.1%
335983248
0.1%
335984219
0.1%
335985246
0.1%
335986240
0.1%
335987241
0.1%
335988205
0.1%
335989255
0.1%
335990248
0.1%
335991250
0.1%
ValueCountFrequency (%)
1178425223
0.1%
117842451
 
< 0.1%
1178423257
0.1%
1178422246
0.1%
1178421242
0.1%
1178420241
0.1%
1178419235
0.1%
1178418244
0.1%
1178417249
0.1%
1178416244
0.1%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.368386
Minimum2008
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:20:47.995568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2013
Q32016
95-th percentile2019
Maximum2019
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.323319105
Coefficient of variation (CV)0.001650626447
Kurtosis-1.126435923
Mean2013.368386
Median Absolute Deviation (MAD)3
Skewness0.07451207835
Sum355506496
Variance11.04444988
MonotonicityIncreasing
2021-09-17T12:20:48.274841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
201318152
10.3%
201217767
10.1%
201117013
9.6%
201014489
8.2%
201414288
8.1%
201814286
8.1%
201614096
8.0%
201713849
7.8%
201513641
7.7%
200913595
7.7%
Other values (2)25397
14.4%
ValueCountFrequency (%)
200813489
7.6%
200913595
7.7%
201014489
8.2%
201117013
9.6%
201217767
10.1%
201318152
10.3%
201414288
8.1%
201513641
7.7%
201614096
8.0%
201713849
7.8%
ValueCountFrequency (%)
201911908
6.7%
201814286
8.1%
201713849
7.8%
201614096
8.0%
201513641
7.7%
201414288
8.1%
201318152
10.3%
201217767
10.1%
201117013
9.6%
201014489
8.2%

batsman
Categorical

HIGH CARDINALITY

Distinct514
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
V Kohli
 
4202
SK Raina
 
3968
RG Sharma
 
3732
S Dhawan
 
3732
G Gambhir
 
3524
Other values (509)
157415 

Length

Max length23
Median length9
Mean length9.347589949
Min length5

Characters and Unicode

Total characters1650532
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowAC Gilchrist
2nd rowAC Gilchrist
3rd rowAC Gilchrist
4th rowY Venugopal Rao
5th rowY Venugopal Rao

Common Values

ValueCountFrequency (%)
V Kohli4202
 
2.4%
SK Raina3968
 
2.2%
RG Sharma3732
 
2.1%
S Dhawan3732
 
2.1%
G Gambhir3524
 
2.0%
RV Uthappa3422
 
1.9%
DA Warner3397
 
1.9%
MS Dhoni3260
 
1.8%
AM Rahane3208
 
1.8%
CH Gayle3073
 
1.7%
Other values (504)141055
79.9%

Length

2021-09-17T12:20:49.106615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v6418
 
1.8%
s6109
 
1.7%
singh4851
 
1.3%
da4771
 
1.3%
sharma4585
 
1.3%
sr4557
 
1.3%
sk4248
 
1.2%
de4242
 
1.2%
kohli4222
 
1.2%
m4128
 
1.1%
Other values (706)313709
86.7%

Most occurring characters

ValueCountFrequency (%)
185267
 
11.2%
a182402
 
11.1%
i80429
 
4.9%
n76242
 
4.6%
h75624
 
4.6%
r71129
 
4.3%
e67135
 
4.1%
S66529
 
4.0%
l62276
 
3.8%
s43513
 
2.6%
Other values (44)739986
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter959376
58.1%
Uppercase Letter505666
30.6%
Space Separator185267
 
11.2%
Dash Punctuation223
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S66529
13.2%
M43242
 
8.6%
R43083
 
8.5%
A41503
 
8.2%
K40834
 
8.1%
D34928
 
6.9%
P34447
 
6.8%
J24301
 
4.8%
G23471
 
4.6%
V22872
 
4.5%
Other values (16)130456
25.8%
Lowercase Letter
ValueCountFrequency (%)
a182402
19.0%
i80429
 
8.4%
n76242
 
7.9%
h75624
 
7.9%
r71129
 
7.4%
e67135
 
7.0%
l62276
 
6.5%
s43513
 
4.5%
t36682
 
3.8%
o36490
 
3.8%
Other values (16)227454
23.7%
Space Separator
ValueCountFrequency (%)
185267
100.0%
Dash Punctuation
ValueCountFrequency (%)
-223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1465042
88.8%
Common185490
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a182402
 
12.5%
i80429
 
5.5%
n76242
 
5.2%
h75624
 
5.2%
r71129
 
4.9%
e67135
 
4.6%
S66529
 
4.5%
l62276
 
4.3%
s43513
 
3.0%
M43242
 
3.0%
Other values (42)696521
47.5%
Common
ValueCountFrequency (%)
185267
99.9%
-223
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1650532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185267
 
11.2%
a182402
 
11.1%
i80429
 
4.9%
n76242
 
4.6%
h75624
 
4.6%
r71129
 
4.3%
e67135
 
4.1%
S66529
 
4.0%
l62276
 
3.8%
s43513
 
2.6%
Other values (44)739986
44.8%

bowler
Categorical

HIGH CARDINALITY

Distinct404
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Harbhajan Singh
 
3352
PP Chawla
 
3133
A Mishra
 
3100
R Ashwin
 
2966
SL Malinga
 
2878
Other values (399)
161144 

Length

Max length23
Median length9
Mean length9.535931315
Min length5

Characters and Unicode

Total characters1683788
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGD McGrath
2nd rowGD McGrath
3rd rowGD McGrath
4th rowGD McGrath
5th rowGD McGrath

Common Values

ValueCountFrequency (%)
Harbhajan Singh3352
 
1.9%
PP Chawla3133
 
1.8%
A Mishra3100
 
1.8%
R Ashwin2966
 
1.7%
SL Malinga2878
 
1.6%
P Kumar2637
 
1.5%
B Kumar2631
 
1.5%
DJ Bravo2620
 
1.5%
UT Yadav2571
 
1.5%
SP Narine2545
 
1.4%
Other values (394)148140
83.9%

Length

2021-09-17T12:20:49.811716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r9587
 
2.7%
singh9132
 
2.5%
sharma9113
 
2.5%
a8379
 
2.3%
kumar7478
 
2.1%
s6076
 
1.7%
m5986
 
1.7%
pp5078
 
1.4%
p4679
 
1.3%
b4124
 
1.1%
Other values (578)290435
80.7%

Most occurring characters

ValueCountFrequency (%)
a214753
 
12.8%
183494
 
10.9%
n90023
 
5.3%
r88641
 
5.3%
h86147
 
5.1%
i73765
 
4.4%
e72562
 
4.3%
S66069
 
3.9%
l54201
 
3.2%
M46001
 
2.7%
Other values (45)708132
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1026217
60.9%
Uppercase Letter473371
28.1%
Space Separator183494
 
10.9%
Dash Punctuation631
 
< 0.1%
Open Punctuation25
 
< 0.1%
Decimal Number25
 
< 0.1%
Close Punctuation25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a214753
20.9%
n90023
 
8.8%
r88641
 
8.6%
h86147
 
8.4%
i73765
 
7.2%
e72562
 
7.1%
l54201
 
5.3%
o39397
 
3.8%
t38891
 
3.8%
m38239
 
3.7%
Other values (16)229598
22.4%
Uppercase Letter
ValueCountFrequency (%)
S66069
14.0%
M46001
9.7%
A41757
 
8.8%
P40690
 
8.6%
K35016
 
7.4%
R33168
 
7.0%
J30768
 
6.5%
B24797
 
5.2%
D22003
 
4.6%
C19609
 
4.1%
Other values (14)113493
24.0%
Space Separator
ValueCountFrequency (%)
183494
100.0%
Dash Punctuation
ValueCountFrequency (%)
-631
100.0%
Open Punctuation
ValueCountFrequency (%)
(25
100.0%
Decimal Number
ValueCountFrequency (%)
225
100.0%
Close Punctuation
ValueCountFrequency (%)
)25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1499588
89.1%
Common184200
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a214753
 
14.3%
n90023
 
6.0%
r88641
 
5.9%
h86147
 
5.7%
i73765
 
4.9%
e72562
 
4.8%
S66069
 
4.4%
l54201
 
3.6%
M46001
 
3.1%
A41757
 
2.8%
Other values (40)665669
44.4%
Common
ValueCountFrequency (%)
183494
99.6%
-631
 
0.3%
(25
 
< 0.1%
225
 
< 0.1%
)25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1683788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a214753
 
12.8%
183494
 
10.9%
n90023
 
5.3%
r88641
 
5.3%
h86147
 
5.1%
i73765
 
4.4%
e72562
 
4.3%
S66069
 
3.9%
l54201
 
3.2%
M46001
 
2.7%
Other values (45)708132
42.1%

innings
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1st
91487 
2nd
85086 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters529719
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1st
2nd row1st
3rd row1st
4th row1st
5th row1st

Common Values

ValueCountFrequency (%)
1st91487
51.8%
2nd85086
48.2%

Length

2021-09-17T12:20:50.514835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-17T12:20:50.725269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1st91487
51.8%
2nd85086
48.2%

Most occurring characters

ValueCountFrequency (%)
191487
17.3%
s91487
17.3%
t91487
17.3%
285086
16.1%
n85086
16.1%
d85086
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter353146
66.7%
Decimal Number176573
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s91487
25.9%
t91487
25.9%
n85086
24.1%
d85086
24.1%
Decimal Number
ValueCountFrequency (%)
191487
51.8%
285086
48.2%

Most occurring scripts

ValueCountFrequency (%)
Latin353146
66.7%
Common176573
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s91487
25.9%
t91487
25.9%
n85086
24.1%
d85086
24.1%
Common
ValueCountFrequency (%)
191487
51.8%
285086
48.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII529719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
191487
17.3%
s91487
17.3%
t91487
17.3%
285086
16.1%
n85086
16.1%
d85086
16.1%

non_striker
Categorical

HIGH CARDINALITY

Distinct509
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
SK Raina
 
4092
V Kohli
 
4061
S Dhawan
 
4034
RG Sharma
 
3771
G Gambhir
 
3740
Other values (504)
156875 

Length

Max length23
Median length9
Mean length9.352426475
Min length5

Characters and Unicode

Total characters1651386
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowJC Buttler
2nd rowAM Rahane
3rd rowAM Rahane
4th rowAM Rahane
5th rowAM Rahane

Common Values

ValueCountFrequency (%)
SK Raina4092
 
2.3%
V Kohli4061
 
2.3%
S Dhawan4034
 
2.3%
RG Sharma3771
 
2.1%
G Gambhir3740
 
2.1%
AM Rahane3457
 
2.0%
RV Uthappa3327
 
1.9%
DA Warner3126
 
1.8%
AB de Villiers2982
 
1.7%
CH Gayle2969
 
1.7%
Other values (499)141014
79.9%

Length

2021-09-17T12:20:51.429410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v6419
 
1.8%
s6288
 
1.7%
sr4754
 
1.3%
sharma4715
 
1.3%
singh4580
 
1.3%
da4490
 
1.2%
sk4342
 
1.2%
m4303
 
1.2%
de4193
 
1.2%
dhawan4153
 
1.1%
Other values (704)313662
86.7%

Most occurring characters

ValueCountFrequency (%)
185326
 
11.2%
a183607
 
11.1%
i80194
 
4.9%
n76211
 
4.6%
h75407
 
4.6%
r71024
 
4.3%
e68065
 
4.1%
S66503
 
4.0%
l61756
 
3.7%
M43881
 
2.7%
Other values (44)739412
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter960341
58.2%
Uppercase Letter505463
30.6%
Space Separator185326
 
11.2%
Dash Punctuation256
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S66503
13.2%
M43881
 
8.7%
R43138
 
8.5%
A41355
 
8.2%
K40763
 
8.1%
P34252
 
6.8%
D34162
 
6.8%
J24290
 
4.8%
G23768
 
4.7%
V23073
 
4.6%
Other values (16)130278
25.8%
Lowercase Letter
ValueCountFrequency (%)
a183607
19.1%
i80194
 
8.4%
n76211
 
7.9%
h75407
 
7.9%
r71024
 
7.4%
e68065
 
7.1%
l61756
 
6.4%
s43366
 
4.5%
t36052
 
3.8%
o35218
 
3.7%
Other values (16)229441
23.9%
Space Separator
ValueCountFrequency (%)
185326
100.0%
Dash Punctuation
ValueCountFrequency (%)
-256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1465804
88.8%
Common185582
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a183607
 
12.5%
i80194
 
5.5%
n76211
 
5.2%
h75407
 
5.1%
r71024
 
4.8%
e68065
 
4.6%
S66503
 
4.5%
l61756
 
4.2%
M43881
 
3.0%
s43366
 
3.0%
Other values (42)695790
47.5%
Common
ValueCountFrequency (%)
185326
99.9%
-256
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1651386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185326
 
11.2%
a183607
 
11.1%
i80194
 
4.9%
n76211
 
4.6%
h75407
 
4.6%
r71024
 
4.3%
e68065
 
4.1%
S66503
 
4.0%
l61756
 
3.7%
M43881
 
2.7%
Other values (44)739412
44.8%

replacements
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct30
Distinct (%)100.0%
Missing176543
Missing (%)> 99.9%
Memory size1.3 MiB
{'role': [{'in': 'Harmeet Singh', 'out': 'RP Singh', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'A Ashish Reddy', 'reason': 'injury', 'role': 'bowler'}]}
 
1
{'role': [{'in': 'Mandeep Singh', 'out': 'AC Gilchrist', 'reason': 'injury', 'role': 'batter'}]}
 
1
{'role': [{'in': 'AT Rayudu', 'out': 'SR Tendulkar', 'reason': 'injury', 'role': 'batter'}]}
 
1
{'role': [{'in': 'AD Mascarenhas', 'out': 'Kamran Khan', 'reason': 'injury', 'role': 'bowler'}]}
 
1
Other values (25)
25 

Length

Max length124
Median length88.5
Mean length87.9
Min length66

Characters and Unicode

Total characters2637
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row{'role': [{'in': 'RG Sharma', 'reason': 'injury', 'role': 'bowler'}]}
2nd row{'role': [{'in': 'PP Chawla', 'reason': 'injury', 'role': 'bowler'}]}
3rd row{'role': [{'in': 'Bipul Sharma', 'out': 'Harmeet Singh', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}
4th row{'role': [{'in': 'BCJ Cutting', 'reason': 'injury', 'role': 'bowler'}]}
5th row{'role': [{'in': 'N Rana', 'reason': 'injury', 'role': 'bowler'}]}

Common Values

ValueCountFrequency (%)
{'role': [{'in': 'Harmeet Singh', 'out': 'RP Singh', 'reason': 'excluded - high full pitched balls', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'A Ashish Reddy', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'Mandeep Singh', 'out': 'AC Gilchrist', 'reason': 'injury', 'role': 'batter'}]}1
 
< 0.1%
{'role': [{'in': 'AT Rayudu', 'out': 'SR Tendulkar', 'reason': 'injury', 'role': 'batter'}]}1
 
< 0.1%
{'role': [{'in': 'AD Mascarenhas', 'out': 'Kamran Khan', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'MC Henriques', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'R Ashwin', 'out': 'Mujeeb Ur Rahman', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'AB de Villiers', 'out': 'SS Tiwary', 'reason': 'injury', 'role': 'batter'}]}1
 
< 0.1%
{'role': [{'in': 'Harbhajan Singh', 'out': 'DL Chahar', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
{'role': [{'in': 'SK Raina', 'reason': 'injury', 'role': 'bowler'}]}1
 
< 0.1%
Other values (20)20
 
< 0.1%
(Missing)176543
> 99.9%

Length

2021-09-17T12:20:52.120561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
role60
18.6%
reason30
 
9.3%
in30
 
9.3%
bowler24
 
7.4%
injury23
 
7.1%
out15
 
4.6%
singh8
 
2.5%
7
 
2.2%
pitched7
 
2.2%
excluded7
 
2.2%
Other values (76)112
34.7%

Most occurring characters

ValueCountFrequency (%)
'480
18.2%
293
 
11.1%
r167
 
6.3%
e160
 
6.1%
o139
 
5.3%
l136
 
5.2%
:135
 
5.1%
n112
 
4.2%
a100
 
3.8%
i95
 
3.6%
Other values (42)820
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1345
51.0%
Other Punctuation690
26.2%
Space Separator293
 
11.1%
Uppercase Letter122
 
4.6%
Open Punctuation90
 
3.4%
Close Punctuation90
 
3.4%
Dash Punctuation7
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r167
12.4%
e160
11.9%
o139
10.3%
l136
10.1%
n112
8.3%
a100
 
7.4%
i95
 
7.1%
u61
 
4.5%
h50
 
3.7%
s49
 
3.6%
Other values (15)276
20.5%
Uppercase Letter
ValueCountFrequency (%)
S22
18.0%
K12
9.8%
R11
9.0%
C10
8.2%
A10
8.2%
M10
8.2%
P7
 
5.7%
J7
 
5.7%
H6
 
4.9%
D6
 
4.9%
Other values (8)21
17.2%
Other Punctuation
ValueCountFrequency (%)
'480
69.6%
:135
 
19.6%
,75
 
10.9%
Open Punctuation
ValueCountFrequency (%)
{60
66.7%
[30
33.3%
Close Punctuation
ValueCountFrequency (%)
}60
66.7%
]30
33.3%
Space Separator
ValueCountFrequency (%)
293
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1467
55.6%
Common1170
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r167
11.4%
e160
10.9%
o139
 
9.5%
l136
 
9.3%
n112
 
7.6%
a100
 
6.8%
i95
 
6.5%
u61
 
4.2%
h50
 
3.4%
s49
 
3.3%
Other values (33)398
27.1%
Common
ValueCountFrequency (%)
'480
41.0%
293
25.0%
:135
 
11.5%
,75
 
6.4%
{60
 
5.1%
}60
 
5.1%
[30
 
2.6%
]30
 
2.6%
-7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'480
18.2%
293
 
11.1%
r167
 
6.3%
e160
 
6.1%
o139
 
5.3%
l136
 
5.2%
:135
 
5.1%
n112
 
4.2%
a100
 
3.8%
i95
 
3.6%
Other values (42)820
31.1%

bowled_over
Real number (ℝ≥0)

HIGH CORRELATION

Distinct180
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.528801685
Minimum0.1
Maximum19.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:20:52.451656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q14.5
median9.4
Q314.4
95-th percentile18.5
Maximum19.9
Range19.8
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation5.677219708
Coefficient of variation (CV)0.5957957669
Kurtosis-1.180961644
Mean9.528801685
Median Absolute Deviation (MAD)4.9
Skewness0.04965397921
Sum1682529.1
Variance32.23082361
MonotonicityNot monotonic
2021-09-17T12:20:52.807722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11491
 
0.8%
0.61490
 
0.8%
0.11490
 
0.8%
0.21490
 
0.8%
3.11490
 
0.8%
2.11490
 
0.8%
0.31490
 
0.8%
0.51490
 
0.8%
0.41490
 
0.8%
1.31489
 
0.8%
Other values (170)161673
91.6%
ValueCountFrequency (%)
0.11490
0.8%
0.21490
0.8%
0.31490
0.8%
0.41490
0.8%
0.51490
0.8%
0.61490
0.8%
0.7364
 
0.2%
0.864
 
< 0.1%
0.913
 
< 0.1%
1.11491
0.8%
ValueCountFrequency (%)
19.95
 
< 0.1%
19.835
 
< 0.1%
19.7239
 
0.1%
19.6969
0.5%
19.51017
0.6%
19.41052
0.6%
19.31079
0.6%
19.21114
0.6%
19.11142
0.6%
18.911
 
< 0.1%

batsman_team
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Mumbai Indians
22149 
Royal Challengers Bangalore
20770 
Kings XI Punjab
20684 
Kolkata Knight Riders
20592 
Chennai Super Kings
19271 
Other values (10)
73107 

Length

Max length27
Median length16
Mean length17.99051384
Min length13

Characters and Unicode

Total characters3176639
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRajasthan Royals
2nd rowRajasthan Royals
3rd rowRajasthan Royals
4th rowRajasthan Royals
5th rowRajasthan Royals

Common Values

ValueCountFrequency (%)
Mumbai Indians22149
12.5%
Royal Challengers Bangalore20770
11.8%
Kings XI Punjab20684
11.7%
Kolkata Knight Riders20592
11.7%
Chennai Super Kings19271
10.9%
Delhi Daredevils18780
10.6%
Rajasthan Royals17147
9.7%
Sunrisers Hyderabad12525
7.1%
Deccan Chargers9034
5.1%
Pune Warriors5443
 
3.1%
Other values (5)10178
5.8%

Length

2021-09-17T12:20:53.504858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings39955
 
9.1%
indians22149
 
5.0%
mumbai22149
 
5.0%
royal20770
 
4.7%
challengers20770
 
4.7%
bangalore20770
 
4.7%
punjab20684
 
4.7%
xi20684
 
4.7%
kolkata20592
 
4.7%
knight20592
 
4.7%
Other values (22)210410
47.9%

Most occurring characters

ValueCountFrequency (%)
a361322
 
11.4%
n263758
 
8.3%
262952
 
8.3%
e238027
 
7.5%
i218932
 
6.9%
s209407
 
6.6%
r182357
 
5.7%
l163077
 
5.1%
g118081
 
3.7%
h108734
 
3.4%
Other values (27)1049992
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2453478
77.2%
Uppercase Letter460209
 
14.5%
Space Separator262952
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a361322
14.7%
n263758
10.8%
e238027
9.7%
i218932
8.9%
s209407
8.5%
r182357
 
7.4%
l163077
 
6.6%
g118081
 
4.8%
h108734
 
4.4%
u92172
 
3.8%
Other values (11)497611
20.3%
Uppercase Letter
ValueCountFrequency (%)
K84303
18.3%
R79136
17.2%
C50633
11.0%
D48152
10.5%
I42833
9.3%
S35276
7.7%
P29607
 
6.4%
M22149
 
4.8%
B20770
 
4.5%
X20684
 
4.5%
Other values (5)26666
 
5.8%
Space Separator
ValueCountFrequency (%)
262952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2913687
91.7%
Common262952
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a361322
 
12.4%
n263758
 
9.1%
e238027
 
8.2%
i218932
 
7.5%
s209407
 
7.2%
r182357
 
6.3%
l163077
 
5.6%
g118081
 
4.1%
h108734
 
3.7%
u92172
 
3.2%
Other values (26)957820
32.9%
Common
ValueCountFrequency (%)
262952
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3176639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a361322
 
11.4%
n263758
 
8.3%
262952
 
8.3%
e238027
 
7.5%
i218932
 
6.9%
s209407
 
6.6%
r182357
 
5.7%
l163077
 
5.1%
g118081
 
3.7%
h108734
 
3.4%
Other values (27)1049992
33.1%

player_out
Categorical

HIGH CARDINALITY

Distinct487
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
167862 
SK Raina
 
157
RG Sharma
 
152
RV Uthappa
 
151
V Kohli
 
142
Other values (482)
 
8109

Length

Max length23
Median length1
Mean length1.413789198
Min length1

Characters and Unicode

Total characters249637
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0167862
95.1%
SK Raina157
 
0.1%
RG Sharma152
 
0.1%
RV Uthappa151
 
0.1%
V Kohli142
 
0.1%
G Gambhir136
 
0.1%
S Dhawan135
 
0.1%
KD Karthik134
 
0.1%
PA Patel125
 
0.1%
AM Rahane115
 
0.1%
Other values (477)7464
 
4.2%

Length

2021-09-17T12:20:54.268817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0167862
90.4%
singh314
 
0.2%
s275
 
0.1%
v257
 
0.1%
r235
 
0.1%
sharma233
 
0.1%
m223
 
0.1%
patel186
 
0.1%
sk184
 
0.1%
sr180
 
0.1%
Other values (671)15754
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0167862
67.2%
a9294
 
3.7%
9130
 
3.7%
i3907
 
1.6%
h3858
 
1.5%
n3789
 
1.5%
r3588
 
1.4%
e3307
 
1.3%
S3223
 
1.3%
l2932
 
1.2%
Other values (45)38747
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number167862
67.2%
Lowercase Letter47864
 
19.2%
Uppercase Letter24758
 
9.9%
Space Separator9130
 
3.7%
Dash Punctuation23
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S3223
13.0%
M2142
 
8.7%
A2111
 
8.5%
R2076
 
8.4%
K1926
 
7.8%
P1803
 
7.3%
D1545
 
6.2%
J1252
 
5.1%
V1074
 
4.3%
G1063
 
4.3%
Other values (16)6543
26.4%
Lowercase Letter
ValueCountFrequency (%)
a9294
19.4%
i3907
 
8.2%
h3858
 
8.1%
n3789
 
7.9%
r3588
 
7.5%
e3307
 
6.9%
l2932
 
6.1%
s2033
 
4.2%
t1891
 
4.0%
o1825
 
3.8%
Other values (16)11440
23.9%
Decimal Number
ValueCountFrequency (%)
0167862
100.0%
Space Separator
ValueCountFrequency (%)
9130
100.0%
Dash Punctuation
ValueCountFrequency (%)
-23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177015
70.9%
Latin72622
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9294
 
12.8%
i3907
 
5.4%
h3858
 
5.3%
n3789
 
5.2%
r3588
 
4.9%
e3307
 
4.6%
S3223
 
4.4%
l2932
 
4.0%
M2142
 
2.9%
A2111
 
2.9%
Other values (42)34471
47.5%
Common
ValueCountFrequency (%)
0167862
94.8%
9130
 
5.2%
-23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII249637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0167862
67.2%
a9294
 
3.7%
9130
 
3.7%
i3907
 
1.6%
h3858
 
1.5%
n3789
 
1.5%
r3588
 
1.4%
e3307
 
1.3%
S3223
 
1.3%
l2932
 
1.2%
Other values (45)38747
 
15.5%

fielder_caught_out
Categorical

HIGH CARDINALITY

Distinct509
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
170319 
MS Dhoni
 
152
KD Karthik
 
151
RV Uthappa
 
123
AB de Villiers
 
113
Other values (504)
 
5715

Length

Max length23
Median length1
Mean length1.302135661
Min length1

Characters and Unicode

Total characters229922
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0170319
96.5%
MS Dhoni152
 
0.1%
KD Karthik151
 
0.1%
RV Uthappa123
 
0.1%
AB de Villiers113
 
0.1%
SK Raina110
 
0.1%
PA Patel95
 
0.1%
RG Sharma90
 
0.1%
V Kohli86
 
< 0.1%
NV Ojha82
 
< 0.1%
Other values (499)5252
 
3.0%

Length

2021-09-17T12:20:54.985899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0170319
92.9%
singh201
 
0.1%
r193
 
0.1%
ms187
 
0.1%
m185
 
0.1%
sharma181
 
0.1%
karthik165
 
0.1%
de160
 
0.1%
s158
 
0.1%
patel158
 
0.1%
Other values (632)11364
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0170319
74.1%
a6773
 
2.9%
6698
 
2.9%
i2973
 
1.3%
h2908
 
1.3%
n2648
 
1.2%
r2633
 
1.1%
e2352
 
1.0%
S2273
 
1.0%
l2107
 
0.9%
Other values (46)28238
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number170319
74.1%
Lowercase Letter34975
 
15.2%
Uppercase Letter17711
 
7.7%
Space Separator6698
 
2.9%
Open Punctuation102
 
< 0.1%
Close Punctuation102
 
< 0.1%
Dash Punctuation15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6773
19.4%
i2973
 
8.5%
h2908
 
8.3%
n2648
 
7.6%
r2633
 
7.5%
e2352
 
6.7%
l2107
 
6.0%
t1519
 
4.3%
s1509
 
4.3%
o1368
 
3.9%
Other values (16)8185
23.4%
Uppercase Letter
ValueCountFrequency (%)
S2273
12.8%
M1556
 
8.8%
K1555
 
8.8%
A1493
 
8.4%
R1439
 
8.1%
P1332
 
7.5%
D1227
 
6.9%
J899
 
5.1%
B834
 
4.7%
V769
 
4.3%
Other values (15)4334
24.5%
Decimal Number
ValueCountFrequency (%)
0170319
100.0%
Space Separator
ValueCountFrequency (%)
6698
100.0%
Open Punctuation
ValueCountFrequency (%)
(102
100.0%
Close Punctuation
ValueCountFrequency (%)
)102
100.0%
Dash Punctuation
ValueCountFrequency (%)
-15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common177236
77.1%
Latin52686
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6773
 
12.9%
i2973
 
5.6%
h2908
 
5.5%
n2648
 
5.0%
r2633
 
5.0%
e2352
 
4.5%
S2273
 
4.3%
l2107
 
4.0%
M1556
 
3.0%
K1555
 
3.0%
Other values (41)24908
47.3%
Common
ValueCountFrequency (%)
0170319
96.1%
6698
 
3.8%
(102
 
0.1%
)102
 
0.1%
-15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII229922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0170319
74.1%
a6773
 
2.9%
6698
 
2.9%
i2973
 
1.3%
h2908
 
1.3%
n2648
 
1.2%
r2633
 
1.1%
e2352
 
1.0%
S2273
 
1.0%
l2107
 
0.9%
Other values (46)28238
 
12.3%

type_out
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
167862 
caught
 
5219
bowled
 
1566
run out
 
844
lbw
 
530
Other values (5)
 
552

Length

Max length21
Median length1
Mean length1.260288946
Min length1

Characters and Unicode

Total characters222533
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0167862
95.1%
caught5219
 
3.0%
bowled1566
 
0.9%
run out844
 
0.5%
lbw530
 
0.3%
stumped280
 
0.2%
caught and bowled250
 
0.1%
retired hurt11
 
< 0.1%
hit wicket10
 
< 0.1%
obstructing the field1
 
< 0.1%

Length

2021-09-17T12:20:55.669054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-17T12:20:55.934343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0167862
94.3%
caught5469
 
3.1%
bowled1816
 
1.0%
out844
 
0.5%
run844
 
0.5%
lbw530
 
0.3%
stumped280
 
0.2%
and250
 
0.1%
hurt11
 
< 0.1%
retired11
 
< 0.1%
Other values (5)23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0167862
75.4%
u7449
 
3.3%
t6638
 
3.0%
a5719
 
2.6%
h5491
 
2.5%
c5480
 
2.5%
g5470
 
2.5%
o2661
 
1.2%
d2358
 
1.1%
w2356
 
1.1%
Other values (12)11049
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number167862
75.4%
Lowercase Letter53304
 
24.0%
Space Separator1367
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u7449
14.0%
t6638
12.5%
a5719
10.7%
h5491
10.3%
c5480
10.3%
g5470
10.3%
o2661
 
5.0%
d2358
 
4.4%
w2356
 
4.4%
l2347
 
4.4%
Other values (10)7335
13.8%
Decimal Number
ValueCountFrequency (%)
0167862
100.0%
Space Separator
ValueCountFrequency (%)
1367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common169229
76.0%
Latin53304
 
24.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u7449
14.0%
t6638
12.5%
a5719
10.7%
h5491
10.3%
c5480
10.3%
g5470
10.3%
o2661
 
5.0%
d2358
 
4.4%
w2356
 
4.4%
l2347
 
4.4%
Other values (10)7335
13.8%
Common
ValueCountFrequency (%)
0167862
99.2%
1367
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII222533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0167862
75.4%
u7449
 
3.3%
t6638
 
3.0%
a5719
 
2.6%
h5491
 
2.5%
c5480
 
2.5%
g5470
 
2.5%
o2661
 
1.2%
d2358
 
1.1%
w2356
 
1.1%
Other values (12)11049
 
5.0%

extras_wides
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03682329688
Minimum0
Maximum5
Zeros171230
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:20:56.369184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2516125522
Coefficient of variation (CV)6.832971881
Kurtosis191.5014881
Mean0.03682329688
Median Absolute Deviation (MAD)0
Skewness11.65890837
Sum6502
Variance0.0633088764
MonotonicityNot monotonic
2021-09-17T12:20:56.632498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0171230
97.0%
14858
 
2.8%
2229
 
0.1%
5207
 
0.1%
345
 
< 0.1%
44
 
< 0.1%
ValueCountFrequency (%)
0171230
97.0%
14858
 
2.8%
2229
 
0.1%
345
 
< 0.1%
44
 
< 0.1%
5207
 
0.1%
ValueCountFrequency (%)
5207
 
0.1%
44
 
< 0.1%
345
 
< 0.1%
2229
 
0.1%
14858
 
2.8%
0171230
97.0%

extras_legbyes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02119803141
Minimum0
Maximum5
Zeros173664
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:20:56.879837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1949347214
Coefficient of variation (CV)9.195887942
Kurtosis241.5230135
Mean0.02119803141
Median Absolute Deviation (MAD)0
Skewness13.74586003
Sum3743
Variance0.03799954562
MonotonicityNot monotonic
2021-09-17T12:20:57.139477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0173664
98.4%
12536
 
1.4%
4216
 
0.1%
2136
 
0.1%
317
 
< 0.1%
54
 
< 0.1%
ValueCountFrequency (%)
0173664
98.4%
12536
 
1.4%
2136
 
0.1%
317
 
< 0.1%
4216
 
0.1%
54
 
< 0.1%
ValueCountFrequency (%)
54
 
< 0.1%
4216
 
0.1%
317
 
< 0.1%
2136
 
0.1%
12536
 
1.4%
0173664
98.4%

extras_noballs
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
175870 
1
 
687
2
 
9
5
 
6
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176573
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

Length

2021-09-17T12:20:57.794748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-17T12:20:58.240534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number176573
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common176573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII176573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0175870
99.6%
1687
 
0.4%
29
 
< 0.1%
56
 
< 0.1%
31
 
< 0.1%

extras_byes
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
176097 
1
 
321
4
 
121
2
 
31
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176573
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

Length

2021-09-17T12:20:58.880823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-17T12:20:59.078295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number176573
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common176573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII176573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0176097
99.7%
1321
 
0.2%
4121
 
0.1%
231
 
< 0.1%
33
 
< 0.1%

extras_penalty
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
176571 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters176573
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

Length

2021-09-17T12:20:59.663730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-17T12:20:59.851229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number176573
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common176573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII176573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0176571
> 99.9%
52
 
< 0.1%

total_extras_runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06721865744
Minimum0
Maximum7
Zeros167142
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:21:00.017786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3430137251
Coefficient of variation (CV)5.102954122
Kurtosis91.09801952
Mean0.06721865744
Median Absolute Deviation (MAD)0
Skewness8.226940401
Sum11869
Variance0.1176584156
MonotonicityNot monotonic
2021-09-17T12:21:00.314989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0167142
94.7%
18401
 
4.8%
2404
 
0.2%
4342
 
0.2%
5218
 
0.1%
365
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0167142
94.7%
18401
 
4.8%
2404
 
0.2%
365
 
< 0.1%
4342
 
0.2%
5218
 
0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
5218
 
0.1%
4342
 
0.2%
365
 
< 0.1%
2404
 
0.2%
18401
 
4.8%
0167142
94.7%

batsman_runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237431544
Minimum0
Maximum6
Zeros71130
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:21:00.616184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.609116193
Coefficient of variation (CV)1.300367847
Kurtosis1.638279999
Mean1.237431544
Median Absolute Deviation (MAD)1
Skewness1.585889292
Sum218497
Variance2.589254921
MonotonicityNot monotonic
2021-09-17T12:21:00.839586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
071130
40.3%
165416
37.0%
420075
 
11.4%
211292
 
6.4%
68035
 
4.6%
3569
 
0.3%
556
 
< 0.1%
ValueCountFrequency (%)
071130
40.3%
165416
37.0%
211292
 
6.4%
3569
 
0.3%
420075
 
11.4%
556
 
< 0.1%
68035
 
4.6%
ValueCountFrequency (%)
68035
 
4.6%
556
 
< 0.1%
420075
 
11.4%
3569
 
0.3%
211292
 
6.4%
165416
37.0%
071130
40.3%

total_runs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.304650201
Minimum0
Maximum7
Zeros62100
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2021-09-17T12:21:01.092910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.597156266
Coefficient of variation (CV)1.224202675
Kurtosis1.579171701
Mean1.304650201
Median Absolute Deviation (MAD)1
Skewness1.555697515
Sum230366
Variance2.550908138
MonotonicityNot monotonic
2021-09-17T12:21:01.329278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
173180
41.4%
062100
35.2%
420337
 
11.5%
211894
 
6.7%
67988
 
4.5%
3672
 
0.4%
5354
 
0.2%
748
 
< 0.1%
ValueCountFrequency (%)
062100
35.2%
173180
41.4%
211894
 
6.7%
3672
 
0.4%
420337
 
11.5%
5354
 
0.2%
67988
 
4.5%
748
 
< 0.1%
ValueCountFrequency (%)
748
 
< 0.1%
67988
 
4.5%
5354
 
0.2%
420337
 
11.5%
3672
 
0.4%
211894
 
6.7%
173180
41.4%
062100
35.2%

Interactions

2021-09-17T12:20:11.710668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:12.119600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:12.567399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:13.056193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:13.469072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:13.888784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:14.334670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:14.773499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:15.216315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:15.691047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:16.131893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:16.643501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:17.488242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:18.421750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:19.221608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:19.812031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:20.391485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:20.863220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:21.330970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:21.837614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:22.418077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:22.912860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:23.420821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:23.902559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:24.376291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:24.846090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:25.350801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:25.926270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:26.408107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:26.815016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:27.247860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:27.680723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:28.126510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:28.594261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:29.019147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:29.454958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:29.848930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:30.268805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:30.714592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:31.325957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:31.782738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:32.235529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:32.698289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:33.152075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:33.583924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:34.016781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:34.470574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:34.917379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:35.372163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:35.808998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:36.241818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:36.685632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:37.087580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:37.490501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:37.936288image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:38.382099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:38.826910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:39.285703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:39.727521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:40.194253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:40.631004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:41.065824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:41.536566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-17T12:20:41.987379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-17T12:21:01.611523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-17T12:21:02.170031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-17T12:21:02.728538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-17T12:21:03.353886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-17T12:21:04.030057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-17T12:20:42.736376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-17T12:20:44.359022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-17T12:20:45.835360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idseasonbatsmanbowlerinningsnon_strikerreplacementsbowled_overbatsman_teamplayer_outfielder_caught_outtype_outextras_widesextras_legbyesextras_noballsextras_byesextras_penaltytotal_extras_runsbatsman_runstotal_runs
03359882008AC GilchristGD McGrath1stJC ButtlerNaN0.1Rajasthan Royals00001000101
13359882008AC GilchristGD McGrath1stAM RahaneNaN0.2Rajasthan Royals00000000000
23359882008AC GilchristGD McGrath1stAM RahaneNaN0.3Rajasthan Royals00000000044
33359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.4Rajasthan Royals00000000000
43359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.5Rajasthan Royals00000000066
53359882008Y Venugopal RaoGD McGrath1stAM RahaneNaN0.6Rajasthan Royals00000000000
63359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.1Rajasthan Royals00000000000
73359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.2Rajasthan Royals00000000000
83359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.3Rajasthan Royals00000000044
93359882008AC GilchristMohammad Asif1stJC ButtlerNaN1.4Rajasthan Royals00000000044

Last rows

idseasonbatsmanbowlerinningsnon_strikerreplacementsbowled_overbatsman_teamplayer_outfielder_caught_outtype_outextras_widesextras_legbyesextras_noballsextras_byesextras_penaltytotal_extras_runsbatsman_runstotal_runs
17656311784242019LS LivingstoneNA Saini2ndMS DhoniNaN18.3Chennai Super Kings00000000011
17656411784242019LS LivingstoneNA Saini2ndSW BillingsNaN18.4Chennai Super Kings00000000000
17656511784242019SV SamsonK Khejroliya2ndSW BillingsNaN18.5Chennai Super Kings00000000022
17656611784242019SV SamsonK Khejroliya2ndSW BillingsNaN18.6Chennai Super Kings00000000044
17656711784242019LS LivingstoneK Khejroliya2ndMS DhoniNaN19.1Chennai Super Kings00000000044
17656811784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.2Chennai Super Kings00000000044
17656911784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.3Chennai Super Kings00000000022
17657011784242019SV SamsonK Khejroliya2ndMS DhoniNaN19.4Chennai Super KingsSW Billings0run out00000000
17657111784242019LS LivingstoneYS Chahal2ndMS DhoniNaN19.5Chennai Super Kings00000000011
17657211784242019SV SamsonYS Chahal2ndDJ BravoNaN19.6Chennai Super Kings00000000011